Работая с сайтом, я даю свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта обрабатывается системой Яндекс.Метрика

Принять

Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology

Online media
issn 2310-6018

submit
a manuscript

metadata of articles for the last 2 years

Efficient resource allocation in geodistributed heterogeneous dynamic computing environments

2024. T.12. № 4. id 1704

Klimenko A.B.

DOI: 10.26102/2310-6018/2024.47.4.011

Currently, the management of computing resources in geo-distributed heterogeneous dynamic computing environments is a non-trivial scientific problem. Due to the complexity of such systems, the distribution of computing resources becomes a computationally hard problem, usually multi-criteria, with nonlinear constraints, integer or mixed-integer. The solution of such problems produces some additional costs of system exploitation. In addition, the property of geo-distribution also introduces additional resource costs that arise during data transit between computing subtasks in the case when transit sections of the network are involved and the route length is more than one section. The purpose of this study is to implement effective management of computing resources based on the criterion of using computing resources – both in the process of their distribution and in solving a computational task in a computing environment. To achieve the goal of the study, a new formulation of the computational resource distribution problem has been developed, which takes into account the properties of heterogeneity, dynamics and geo-distribution of the computing environment and is distinguished by the presence of controlled parameters that determine the resource costs both for data transmission over the network and for solving the computational resource distribution problem. A method has been developed that allows solving the formulated problem, which includes the stages of developing a metaheuristic repository and its use. The results of the conducted modeling allow us to conclude that the developed method is promising – the computing resource usage for resources distribution has decreased by 28 times with a loss in the quality of the resulting solution of up to 10%.

Keywords: resource allocation, distributed computing, distributed computing management, dynamic computing environment, optimization, metaheuristics

GIS-oriented classification modeling for management in organizational systems with a heterogeneous structure of spatial elements

2024. T.12. № 3. id 1702

Linkina A.V. Ryndin N.A.

DOI: 10.26102/2310-6018/2024.46.3.030

The article presents theoretical approaches to formalizing problems of optimizing the management of complex organizational systems, taking into account GIS-based classification modeling. It is shown that models of complex systems with spatial characteristics can be classified as stochastic due to the wide variability of input parameters and their random distribution (both in space and time). At the same time, it is clarified that spatial characteristics can be considered, in fact, both geographic reference and any other attribute information about the objects of the system under consideration. The problem of presenting a model of a complex organizational system of an agricultural profile is solved, taking into account the hierarchy of characteristics affecting the system. It is clarified that a feature of the system under consideration is the dependence of stability not only on the structure and parameters of the system (as for linear systems), but also on the magnitude of the initial deviation of the system from the equilibrium position, based on the phase space method, widely used in the theory of automatic control. The problem of finding the optimal (equilibrium) state of a complex organizational system of an agricultural profile is formalized, the choice of significant characteristics and their combined influence on the target variable are justified. 3 main types of input variables are defined. It has been studied that, taking into account the Pareto efficiency when predictors influence each other, the constructed model will make it possible to find optimal solutions in a multicriteria system, taking into account the ranking of the significance and weight of features. The possibility of complicating this problem is noted by the fact that with GIS-oriented classification modeling, the heterogeneous structure of spatial elements can solve the inverse problem - finding the system at a minimum in the case where the optimal option is considered to be the absence of influence on the system of individual input parameters when leveled by other input features.

Keywords: optimization of management of complex systems, GIS-oriented approach, classification modeling, formalized information model, spatial features

Comparison of the efficiency of the random forest algorithm and artificial neural networks of the RNN class in the problem of managing the process of structural-parametric synthesis of business process models based on a genetic algorithm

2024. T.12. № 4. id 1701

Petrosov D.A.

DOI: 10.26102/2310-6018/2024.47.4.020

The results presented in this study are relevant for solving the problem of increasing the efficiency of the genetic algorithm in problems related to the use of big data. In the framework of most existing approaches to the application of the evolutionary procedure, efficiency improvement methods are used that are based on classical approaches aimed at pre-setting the operating parameters of the genetic algorithm operators in a specific subject area. At the same time, when working with big data, there is a need to stop and restart the genetic algorithm to obtain the best solutions, since the population of the evolutionary algorithm can be in local extremes and / or the efficiency of the increase in the quality of individuals does not allow finding the required solution in a given time interval. In this case, it becomes relevant to develop new methods that allow you to manage the search process. One of the approaches to solving this problem is the use of the mathematical apparatus of artificial neural networks of the RNN class, which have proven their effectiveness in solving the classification problem and can be used to identify the state of the population of the genetic algorithm. In addition to the approach based on the use of artificial neural networks, it is relevant to assess the possibility of using the "random forest" algorithm to solve the problem of recognizing the state of a population and making decisions on changing the operating parameters of the genetic algorithm operators directly in the process of work, which will allow influencing the trajectory of the population in the solution space. Within the framework of this article, the results of computational experiments on solving the problem of classifying the state of a population of a genetic algorithm by two modern methods will be considered: the "random forest" algorithm and the artificial neural network RNN, the modeling of which is performed using a graph approach based on the theory of Petri nets, which will allow combining the developed models with the model of a genetic algorithm adapted to solving the problem of structural-parametric synthesis using nested Petri nets.

Keywords: mathematical modeling, business processes, systems analysis, petri net theory, genetic algorithm, artificial neural networks, random forest algorithm

Method for solving the problem of planning jobs in an IT project and assigning specialists to it

2024. T.12. № 4. id 1698

Dyatchina A.V. Oleinikova S.A.

DOI: 10.26102/2310-6018/2024.47.4.009

This article is devoted to the development of an iterative approach that provides a simultaneous solution to the problem of planning individual works of an IT project with the assumption of their possible correction and assignment of specialists to these works. At present, the class of project management problems has been studied in sufficient depth, and various methods for forming schedules from the point of view of various criteria (fastest completion, cost, etc.) have been obtained. However, IT projects differ from standard projects in the periodic revision of tasks (error correction, clarification of the result with the customer, etc.), which requires changes in the mathematical apparatus of the problem. In addition, the time of execution of a particular work will depend on its performer. This feature is taken into account extremely rarely, which allows the decision maker to solve the planning problem and the assignment problem separately. However, the analysis of the subject area shows that not only the duration of a specific work will depend on the specialist, but also the probability of its error-free execution the first time. Therefore, it is advisable to take such features into account when simultaneously solving the planning problem and the assignment problem. In this regard, it is necessary to develop a method that allows taking into account these nuances of the problem under study and ensures the best solution to both the planning and assignment problem from the point of view of the objective functions. The method is based on a combination of basic approaches to solving the assignment problem with the critical path method and PERT. As a result, an iterative algorithm for solving the problem of forming a project schedule and assigning performers to all work was obtained, taking into account the stochastic nature of the time of execution of individual works, as well as their possible correction.

Keywords: project management, assignment problem, random service time, PERT, correction of jobs

Developing a software architecture to support decision making when selecting design strategies from multiple alternatives

2024. T.12. № 4. id 1696

Kalach A.V. Borzykh N.Y. Smolentseva T.E.

DOI: 10.26102/2310-6018/2024.47.4.004

The article examines the stages of building software architecture for multi-criteria analysis of design strategies, taking into account the competencies of decision-makers (DMs). The software considered in the work is based on an algorithm for managing the input set of criteria and is aimed at automating the process of selecting the optimal strategy in project organizations. The logical structure of a relational database is described, ensuring efficient storage and processing of information about DMs, criteria, alternatives, and their evaluations. A modular software architecture implemented in C# using the .NET Framework and the MVVM pattern is presented. Special attention is paid to the multi-criteria analysis module, which implements a combination of the Analytic Hierarchy Process, PROMETHEE, and TOPSIS methods, allowing for various aspects of multi-criteria optimization to be taken into account. The software provides flexible tools for managing criteria, considers the interests of various DMs, and easily adapts to changes in preferences. The results of a comparative analysis of the developed product's efficiency are presented, demonstrating a significant reduction in time for strategy analysis compared to manual processing. The proposed software architecture aims to improve the accuracy and validity of decisions made, reduce time and resource costs, and enhance project management quality in conditions of multi-criteria and uncertainty.

Keywords: multi-criteria analysis, decision support, software, DMs, AHP, PROMETHEE, TOPSIS, modular architecture, project organizations

Concept of dynamic positioning system for unmanned small-class underwater vehicles based on visual odometry

2024. T.12. № 3. id 1695

Aliagaev A.R. Azhmukhamedov I.M. Khomenko T.V.

DOI: 10.26102/2310-6018/2024.46.3.029

The article is devoted to the actual problem of underwater robotics - the problem of dynamic positioning of unmanned underwater vehicles of small class. Particular attention is paid to the methods of navigation of unmanned underwater vehicles and methods for creating a dynamic positioning system, including methods for the synthesis of an observer, a regulator and methods for distributing control actions on the propulsion and steering complex of unmanned underwater vehicles. It is revealed that in the existing dynamic positioning systems, expensive hydro acoustic navigation systems and Doppler speed meters are mainly used to generate feedback on the position and speed of unmanned underwater vehicles. Not all unmanned submersibles of the small class of the budget segment are equipped with such systems, while video systems and inertial sensors are present in almost every device. With the development of onboard computing facilities, it becomes possible to use visual odometry algorithms for navigation of unmanned underwater vehicles based on data from a video system as an alternative to hydro acoustic navigation in the task of dynamic positioning. The concept of architecture of the system of dynamic positioning of unmanned underwater vehicles of small class based on visual odometry is proposed, which helps to reduce the cost of navigation equipment and allows to increase the productivity of underwater technical work.

Keywords: dynamic positioning, unmanned underwater vehicle, navigation system, visual odometry, control system

Approximation of an elliptic operator with a singularity in the space of functions specified on the graph

2024. T.12. № 4. id 1671

Prikhodko I.V. Perova I.V. Gunkina A.S. Part A.A.

DOI: 10.26102/2310-6018/2024.47.3.003

Was proposed an approach to approximation of an elliptic operator used in describing mathematical models of transfer processes of continuum and in problems of controlling elastic vibrations of network-like structures. To ease the problem of studying the presented material, i.e. to simplify the mathematical symbolism of grid functions, the space variable of functions of the domain of definition of the elliptic operator changes on the oriented geometric graph - star, which is not a restrictive circumstance, because an arbitrary graph (in applications – a network) is a collection of stars that differ from each other only in the quantity of edges. An algebraic system and its corresponding finite-dimensional operator are formed, the properties of this operator are established and examples of constructing (and analyzing) difference schemes for the heat transfer equation and the oscillation equation (wave equation) with a space variable changing on a graph (network) are given. In this case, the optimal control problem is reduced to a finite moment problem, which opens the way to obtaining a numerical analysis that does not depend on the dimension of the control vector, it is only necessary to know a limited number of grid eigenfunctions of the finite-difference analogue of the elliptic operator.

Keywords: elliptic operator on a graph, finite-dimensional analog, difference scheme with singularities, optimization of the elliptic operator

Mathematical support for selecting directions for the development program of an organizational system based on a combination of a randomized search algorithm and a genetic algorithm with adaptation

2024. T.12. № 4. id 1668

Ivanov D.V. Lvovich Y.E.

DOI: 10.26102/2310-6018/2024.47.4.013

The article is devoted to the development of an optimization approach to the selection of directions for the optimization system development program. It is shown that the formalization of the process of optimal selection of a management decision when forming a development program leads to a model of multi-alternative optimization. It is advisable to implement the solution of the optimization problem using a directed randomized search. However, in this case it is only possible to form a set of dominant options, which requires the use of expert assessment to select the final option for distributing organizational system objects between the directions of the development program. Another approach is proposed based on a combination of a randomized search algorithm and a genetic algorithm with adaptation. In order to integrate these algorithms into a single iterative scheme for searching for an optimal solution, first of all, the condition for the transition from the first iterative process of a randomized search to the formation of a genetic algorithm population with elements corresponding to random values of alternative variables is substantiated. Parents are selected from this population and a transition to the second iterative process of probabilistic selection of the best option for combining crossbreeding and reproduction schemes is carried out. It is shown that a two-level adaptive algorithm using the values of the fitness function corresponding to the structure of the original optimization problem is acceptable for correcting the probability characteristics from one iteration process. The third iteration process is aimed at including seven mutation options in the selection of genetic algorithm elements. It is shown by what condition the listed search processes are stopped for the subsequent selection of the optimal management solution.

Keywords: organizational system, development program, multi-alternative optimization, randomized search, genetic algorithm, adaptation

Comparison of optimization methods in simulation models of complex organizational and technical systems

2024. T.12. № 3. id 1665

Beketov S.M. Zubkova D.A. Redko S.G.

DOI: 10.26102/2310-6018/2024.46.3.027

The relevance of the study is due to the need to improve the effectiveness of management decisions in complex organizational and technical systems. The problem of this study is to choose the most appropriate optimization method for specific tasks of organizational systems. The purpose of the article is to compare modern methods of optimization of complex organizational and technical systems, in particular, in the model of the transport system. Special attention is paid to minimizing the target function, which takes into account such parameters as passenger traffic, passenger waiting time, vehicle loading and the impact on the traffic situation. The study analyzed suitable optimization methods and implemented software implementation of optimization approaches for the transport system in the Python programming language. The practical part allows evaluating the effectiveness of each method in terms of the results of the objective function, the adequacy of the selected model parameters and the execution time of the algorithm. The results showed that the methods of particle swarm and differential evolution provide the best minimization of the objective function with optimally selected parameters of the range of motion, speed and capacity of the vehicle, however, these optimization methods require a lot of time for calculations. The materials of the article are of practical value for specialists in the field of process optimization and transport planning, offering recommendations on the choice of optimization methods depending on the goals and conditions of the task.

Keywords: optimization methods, organizational and technical system, simplex method, annealing method, double annealing method, differential evolution method, particle swarm method

Algorithm of formation of training and test samples for data character analysis

2024. T.12. № 4. id 1663

Chirkov A.V.

DOI: 10.26102/2310-6018/2024.47.4.014

The article presents an adaptive algorithm for forming training and test datasets for the ANFIS system, used to diagnose the technical condition of electrical equipment. A key feature of the proposed approach is the consideration of temporal dependencies and anomalous data, which enhances the accuracy and completeness of identifying faulty equipment states. The process of testing the algorithm on synthetic data, including vibration, temperature, current, and voltage parameters, is described. The conducted analysis shows that adaptive data partitioning improves the system's ability to identify anomalies compared to the classical method of dataset partitioning. The algorithm is highly applicable for equipment diagnostics in industries where it is crucial to account for dynamic changes in parameters and rare anomalous events.To assess the algorithm's efficiency, it was compared with traditional dataset partitioning methods. The experiment demonstrated that the proposed method enhances the accuracy of classifying anomalous equipment states. Additionally, the algorithm reduces the likelihood of false positives when detecting faults. A notable feature of the development is its ability to adapt to various types of equipment, making it a universal solution for diagnostics in different industrial sectors. The algorithm's future applications are related to its integration into predictive maintenance and monitoring systems, which will increase equipment reliability and reduce repair and maintenance costs.

Keywords: ANFIS, neuro-fuzzy model, adaptive dataset formation, equipment diagnostics, time series, anomalous data, industrial diagnostics, electrical equipment

Mathematical modeling of relations between agents of an organizational system

2024. T.12. № 4. id 1661

Rossikhina L.V. Betskov A.V. Makarov V.F. Kondratiev V.D.

DOI: 10.26102/2310-6018/2024.47.4.001

The article discusses the main types of relationships (conflict, assistance and independence) active agents, the manifestation of which is possible when they interact in the organizational system. The agent's activity is understood as the possibility of independent goal-setting, according to which he chooses actions and his unscrupulous behavior. To characterize active agents, the concept of a utility function is introduced, which determines the agent's choice of actions that allow its usefulness to be maximized, as a rule, this is profit. The mathematical formalization of the relations of active agents is given for the option of achieving the common goal of the organizational system, as well as taking into account the achievement of local goals by active agents. To describe the interaction of active agents in the process of achieving a common goal, a matrix of the state of the organizational system is proposed, which allows to identify the existing cores of conflict, independence and assistance between active agents. The elements of the matrix are quantitative estimates of the set of agent relationships. To determine quantitative estimates of the set of agent relationships, an algorithm based on the calculation of the relative discrepancy of utility functions has been developed, which allows determining the nature and degree of agent relationships. The author's classification of agent relations according to the degree of their manifestation is proposed. An example illustrating the practical implementation of the algorithm is given.

Keywords: agent, multiple relationships, conflict, assistance, independence, utility function, quantitative assessment of relationships, matrix of the state of the organizational system

Development of intelligent models for proactive protection of critical infrastructure of the financial sector using the example of information support for contract systems

2024. T.12. № 4. id 1652

Korchagin S. Rubtsov D. Bespalova N. Serdechny D.

DOI: 10.26102/2310-6018/2024.47.4.005

The paper proposes an approach to developing intelligent models of proactive protection focused on information support of contract systems in the financial sector. A methodology for developing intelligent models is presented, which includes components for monitoring, forecasting and preventing cyberattacks. The proposed methodology formed the basis for practical implementation in Python using the Numpy and Scirket Learn libraries. Particular attention is paid to the use of advanced machine learning and artificial intelligence algorithms to identify and prevent potential threats in real time. As a practical example, the application of the developed intelligent models to protect the information support of contract systems used in the financial sector is considered. Key vulnerabilities, potential attacks and methods for their proactive detection and blocking are analyzed. The results of the study are confirmed by the data of a computational experiment and demonstrate the high efficiency of the proposed approach in increasing the resilience of the critical information infrastructure of the financial sector to cyberattacks. The implementation of intelligent models of proactive protection allows us to significantly reduce the risks of compromising the integrity and availability of key data, minimize financial and reputational losses, and predict and prevent potential threats.

Keywords: mathematical modeling, cybersecurity, intelligent models, proactive defense, financial sector, government contracts, critical information infrastructure

Fractal approach to Monte Carlo based numerical simulation of photon transport in biological tissues

2024. T.12. № 3. id 1648

Potlov A.Y.

DOI: 10.26102/2310-6018/2024.46.3.022

The paper presents a computationally efficient approach to mathematical modeling of the photon migration process in biological tissues. In this case, the tissues of living organisms are described as strongly scattering media with pronounced anisotropy and a relative refractive index higher than that of air. The proposed approach is a modified version of the Monte Carlo statistical testing method, in connection with which the calculation of the photon mean free path, the probability of an absorption or scattering act, energy loss during an absorption act, a new direction of motion in the case of an act of scattering and the behavior of a photon at the boundary of the modeled object or its separate relatively isolated section are performed according to classical formulas. The main distinctive feature of the proposed solution is the description of a photon packet as a tree-like fractal. In this case, the reference trajectory is calculated in the classical way, and the rest are completed according to the principle of self-similarity, adjusted for the presence or absence of areas of abrupt change in optical properties. This approach allows increasing the computing performance by reducing the number of photons in a packet with a proportional increase in the number of packets under consideration. The proposed solution is intended for use in the development of new and improvement of known methods of optical tomography and elastography.

Keywords: mathematical modeling, high-performance computing, biological tissues, optical tomography, optical elastography, monte Carlo method, photon trajectories, fractals

Ensuring functional reliability of telecommunication systems based on topological resource

2024. T.12. № 3. id 1647

Gvozdev V.E. Guzairov M.B. Rakipova A.S. Galimov R.R. Prykhodko V.E. Teplyashyn P.N.

DOI: 10.26102/2310-6018/2024.46.3.024

Modern special-purpose communication and computing systems perform tasks, first of all, to deliver information between spatially distributed bodies involved in solving network-centric control problems. Modern communication and computing systems are characterized by a transition to a hybrid structure, a decentralized network architecture, which predetermines the formation of a single information space based on the integration of different departmental affiliations information systems, and created on the basis of various methodological and technological platforms. In this work, topological and resource approaches are used as approaches that allow us to study the properties of local information systems from a unified methodological position. The conceptual basis was the proposition that a promising approach to routing in conditions of dynamic changes in the state of a telecommunication system is the formation of a backup message delivery paths set, which will increase the reliability and stability of the system. The features of the backup paths formation are determined, limiting the possibility of mechanical transfer of backup methods developed for technical systems to the TCS area. A metric has been proposed that allows one to analyze possible paths for transmitting messages between the source node and the destination node based on a set of static and dynamic characteristics.

Keywords: communication and computing systems, functional reliability, telecommunication systems, topology, routing, dynamic structure

The forecast of the prevalence of cancer among residents of the Moscow region based on a regression model

2024. T.12. № 3. id 1644

Stepanov V.S.

DOI: 10.26102/2310-6018/2024.46.3.023

The article makes an attempt to identify the relationship between cancer prevalence in urban areas and several environmental factors, taking into account a demographic indicator. The regression dependence of the prevalence of oncologic diseases in the territories of urban districts of the Moscow region and several districts of the capital with the proportion of elderly residents and a number of sanitary and hygienic indicators of the territories has been established. The complex of factor explanatory variables included the indicator of atmospheric air pollution of the territory, two variables with the concentration of surface ozone and benz(a)pyrene on it, qualitative variables in terms of the level of its man-made pollution and the volumes of polluted water discharge, the proportion of elderly population. Daily cigarette smoking by adults is also taken into account. On this basis, a regression model with a variable structure is constructed, which has a determination coefficient of 98.5% and an approximation error below 2%. The model parameters were estimated using the least squares method based on data for 51 urban districts of the region and 5 districts of Moscow. The presence of lags in the factors makes it possible to make a forecast of the number of people suffering from tumors of any localization, in the municipal context and with a planning horizon of 1 year. Based on the created model, it is possible to plan primary prevention measures more effectively and allocate medical resources.

Keywords: regression model, atmospheric air pollution, discharge of polluted waste water, benz(a)pyrene, surface ozone, suspended particles, technogenic pollution, malignant neoplasm, city district, municipality

Development of a mivar expert system for planning shop resources and analysis of deviations

2024. T.12. № 3. id 1641

Varlamov O.O. Zhang X. Baldin A.V. Myshenkov K.S. Sidorenko E.V.

DOI: 10.26102/2310-6018/2024.46.3.017

To create mechanical engineering artificial intelligence, mivar technologies of logical artificial intelligence are used. The production process is often accompanied by a large number of events, and various types of deviations and interference directly or indirectly affect the stable and efficient operation of production, and also lead to a decrease in product quality. Predicting variances and disturbances in production planning is a research problem that is the basis of resource planning for production systems. There is a known approach to solving optimization problems of resource allocation of production systems based on the construction of logical inference in a mivar knowledge base, which represents a resource allocation plan. This paper analyzes the deviations and/or disturbances caused by production interference on the shop floor, namely materials, personnel, equipment, processes, and so on, and proposes a definition of production interference in the shop floor production environment. A significant degree of interference results in delays in product deliveries, reductions in quality levels and other deviations from the planned production plan. A mivar expert system has been developed to predict deviations in production processes after planning workshop resources. The expert system was developed using the software package KESMI Wi!Mi "Razumator". Deviations in the production environment were analyzed, a system of factors influencing deviations was established, and a corresponding mivar model for predicting production deviations in the workshop was built. The use of a mivar expert system effectively and quickly solves the problem of decision support based on flexible complex calculations when calculating weights. Therefore, the mivar expert system plays a critical role in predicting interference in the planning of workshop operations, significantly increasing the efficiency of the entire enterprise management system.

Keywords: mivar networks, mivar expert system, decision support system, KESMI, razumator, big knowledge, optimization, distribution of production resources of the workshop, deviations in production processes

Features of applying deep learning methods to detect small objects in video in rainy conditions

2024. T.12. № 3. id 1640

Shtekhin S.E. Stadnik A.V.

DOI: 10.26102/2310-6018/2024.46.3.019

This paper discusses methods for detecting small objects in video when recognizing manual labor operations that take place outdoors, in the open air, and are affected by weather conditions. Approaches to improve the accuracy of detecting such objects in adverse weather conditions, such as rain, are considered. This paper explores a two-stage approach. At the first stage, computer vision methods and deep learning methods such as convolutional neural networks are used to identify and classify various weather conditions in video. At the second stage, when adverse weather conditions are detected, a study is conducted of various deep learning methods for filtering weather conditions in video. The main focus is on assessing the impact of various filtering methods on the accuracy of detecting small objects. The paper considers the applicability of this approach to detecting small tools in video data when recognizing manual labor operations performed during repair and maintenance of a railway track. The obtained results can be useful in the study of labor processes occurring outdoors, in algorithms for recognizing manual labor operations in video data.

Keywords: deep learning, transformer, object detection, recognition of weather conditions on video, filtering of weather conditions, filtering of noise in the image, neural networks, technological operations

Artificial intelligence technologies in the rehabilitation of people with disabilities: analysis of the publication flow

2024. T.12. № 3. id 1638

Sufelfa A.R. Petrishcheva K.N. Shcherbina K.K. Ponomarenko G.N.

DOI: 10.26102/2310-6018/2024.46.3.026

Artificial intelligence technologies are actively used in medicine, which significantly expands the possibilities of disease prevention, diagnosis, treatment and monitoring. Rehabilitation of the disabled, located at the intersection of medicine and the social sphere, traditionally adopts innovative development approaches from the healthcare sector. The issues of using artificial intelligence technologies in the rehabilitation of the disabled, taking into account the specifics of rehabilitation measures for different patients, require study. The purpose of the work is to analyze the foreign studies on the topic of using artificial intelligence technologies in the rehabilitation of the disabled and to identify the most used artificial intelligence methods for subsequent implementation in practice. Publications from the international medical database PubMed over the past 5 years (from January 2019 to May 2024) were analyzed. According to the analysis among artificial intelligence technologies broken down by information processing method, some of the main ones were machine learning, deep learning and neural networks, with different ways of combining all three methods. Most often, these methods are used to create health monitoring and prediction systems (based on machine learning) and (medical) decision support systems (based on neural networks). They have a high potential for use in the rehabilitation of people with disabilities in the areas of medical and social examination, developing individual rehabilitation programmes and monitoring the effectiveness of rehabilitation measures.

Keywords: artificial intelligence, data processing methods, machine learning, rehabilitation, people with disabilities, publication analysis, decision support system, health indicators monitoring

Language models and ontologies, security threats in distributed system

2024. T.12. № 3. id 1634

Donskikh N.I.

DOI: 10.26102/2310-6018/2024.46.3.016

Research in the field of large language models and natural language processing systems has intensified due to the emergence of new, latent and serious risks, for example, violations of the output generation processes, malicious requests in automatic mode. Synergistic scenarios for large language models are being developed. The main hypothesis taken into account in this study is the possibility of insurance (with a given probability) from the generation of prohibited content and its "mixing" with the user query, taking into account ontological properties and connections to improve the quality of search in practical tasks, for example, using an ontology library. Methods of analysis-synthesis, modeling-forecasting, expert-heuristic, probability theory and decision-making were used. The main results of the article: 1) analytics on the problems of applying large language models in achieving stability in the system infrastructure (a table of key methods was proposed); 2) a language model of network infrastructure stability based on estimates of distributions when mixing words is proposed, which uses the Bayesian method; 3) a similar language model was proposed and studied on the basis of an expert-heuristic approach to assessing risks (uncertainties in the system), in particular, using an information-entropy approach. Research can be developed by complicating models (hypotheses) and the "depth" of risk accounting.

Keywords: large language models, resilience, risks, information security, governance

Parametric model of a hose cable using Siemens NX

2024. T.12. № 3. id 1633

Shevchenko D.S.

DOI: 10.26102/2310-6018/2024.46.3.018

Hose cable is one of the key management tools, for example in a subsea oil and gas production system. It can be considered as a customized product related to specific parameters of use cases, such as installation location. This paper applies a method to calculate the reliability of the hose cable using the Advanced First Order Second Moment Method (AFOSM) and Monte Carlo method. The advantages and current limitations of adopting a knowledge-based engineering (KBE) approach are discussed, which in turn enables the creation of different product configurations and variants, for the integration of CAD models augmented with an automatic calculation function. Recommendations are made for future research into the KBE method of product design. The paper demonstrates the use of Siemens NX and its framework for representing engineering knowledge called Knowledge Fusion (KF) to create a reliability-aware parametric model of a hose cable design to improve the sectional design process. The benefits of adopting a KBE approach to integrate CAD models augmented with automatic calculations to ensure product reliability are disclosed, and options for extending the work to consider more complex engineering processes are proposed.

Keywords: parametric model, KBE, knowledge Fusion, CAD, product design, customized product, hose cable, AFOSM, monte Carlo method

Formalization of the computer three-dimensional graphics rendering optimization problem as a variant of the multidimensional knapsack problem

2024. T.12. № 3. id 1632

Mymlikov V. Antamoshkin O.A. Farafonov M.M.

DOI: 10.26102/2310-6018/2024.46.3.014

The work is devoted to solving the problem of optimizing the rendering of computer three-dimensional graphics, namely the rendering pipeline. This work reduces the mentioned problem to a multidimensional version of the well-known combinatorial optimization knapsack problem. The central element of this optimization is capacity, which in the current context is the user's limited hardware capabilities, and the items to be placed in the capacity, which are various pixel shaders. The capacity is limited by the values of the hardware resources, and the shader items have two properties - utility, expressed in some value of contribution to the quality of render, and weight, which is their computational cost. The main challenge in such a context is to develop a system that will be able to solve such a knapsack problem in real time, in order to determine at each moment the best possible combination of shaders. Thus, it will be possible to obtain the best image quality and avoid downtime or overloading of the hardware. The main application of the described system will be in the sphere of computer games, web advertising, movie making and other spheres using computer graphics. Among the key problems in the development of the described system is the difficulty in determining the contribution of each individual shader to the result, due to the it’s subjectivity. Another difficulty is finding a compromise between the accuracy of the knapsack problem solution and the speed of obtaining the result, taking into account the condition that the system must work in real time and not slow down the program for which the optimization is being performed.

Keywords: knapsack problem, rendering, 3D graphics, render pipeline, optimization, neural networks

Assessment of the maturity level of the information security monitoring center in the context of ensuring the sustainability of risk management

2024. T.12. № 3. id 1631

Ponomarev A.V.

DOI: 10.26102/2310-6018/2024.46.3.015

Assessment of the effectiveness of the security monitoring and management centers is an urgent task, the solution of which depends on both the reliability of the entire system and monitoring and forecasting. The purpose of the work is to conduct a systematic analysis of factors and metrics (indicators) affecting the maturity level of monitoring centers. This problem is realized by identifying control parameters and predicting (modeling) the stability of risk management of centers when servicing requests. In particular, the formation of an integral stability index is of interest. The hypotheses of the study are considered an acceptable "tolerance band," control stability, attack planning and vulnerability analysis, the need for situational modeling. Methods of system analysis and synthesis, modeling, probability theory, heuristic approach were used. The main results of the article: 1) analysis of the sustainability of information and economic security policies and classification of direct and indirect threats in the digital business ecosystem; 2) based on the analysis done, an adaptive scheme for modeling the risk stability of a corporate system and a formal optimization model for predicting sustainable protection (based on the cost of ensuring the required security measure) were proposed; 3) as practical applications, a probabilistic model of servicing requests in a distributed system (at a given intensity of "mixing" requests of intruders) and a heuristic procedure for assessing the level of stability monitoring are proposed. The work is developed in the direction of complication of models, their elasticity and "depth" of risk accounting.

Keywords: assessment, sustainability, maturity, information security center, monitoring, risk, management

The use of artificial neural networks to search for objects in medical images

2024. T.12. № 3. id 1630

Rudenko A.V. Rudenko M.A. Kashirina I.L.

DOI: 10.26102/2310-6018/2024.46.3.013

The article is devoted to the use of artificial neural network technologies to identify objects in medical images, including images of human internal organs obtained as a result of a computed tomography procedure. The purpose of this study was to select a method for analyzing medical images and its implementation in a decision support system in surgery and urology when diagnosing human urolithiasis. The article examines the applicability of classification, detection and segmentation methods for solving various problems of object detection in medical images. It has been shown that detection is best suited for use in a medical decision support system for diagnosing urolithiasis for the purpose of planning further surgical intervention. Therefore, the article discusses the main modern neural network architectures applicable to solving the detection problem. To solve the problem of detecting objects in medical images obtained from the results of computed tomography of human internal organs, the feasibility of using a neural network of the YOLO architecture is justified. Based on the results of a computational experiment, problem areas associated with the detection of kidney objects and stones by the YOLO network were identified. To increase the accuracy of the method, it is proposed to use an algorithm for fuzzy estimation of object detection results using a neural network of the YOLO architecture. The results of image detection by the YOLO neural network after its modification allow further calculations of the parameters of the found objects for planning surgical intervention.

Keywords: computer vision, medical images, classification, detection, segmentation, neural networks, computed tomography, urolithiasis

A method for generating contours that preserve the distribution characteristics of geometric parameters from a training set using polar representation of contours

2024. T.12. № 3. id 1626

Kalashnikov V.A.

DOI: 10.26102/2310-6018/2024.46.3.012

This article presents a new algorithm for visual data augmentation based on statistical methods. The method includes an original approach to encoding contours as one-dimensional vectors, storing information about distances from the center of gravity to vertices at specific angles. An algorithm for generating new contours is proposed, based on the statistical characteristics of the original dataset and normal distribution. The key feature of the method is the preservation of important statistical properties of the original dataset, which is confirmed by mathematical proofs of two main statements about the invariance of mathematical expectation and variance. A visual example demonstrating the method's performance on a real contour is presented. The proposed approach has potential applications in various fields, including computer vision, medical imaging, and remote sensing, where generation and augmentation of object contour data play a crucial role. The method can be particularly useful in situations where collecting real data is difficult or resource-intensive. The main results were obtained through an analytical method – the developed mathematical model is supplemented by a random number generator from a distribution with parameters calculated based on the training dataset. The parameters are selected in such a way that the main statistical characteristics of the training dataset are preserved in the synthetic data, allowing for effective application of the proposed algorithm to a wide class of pattern recognition tasks.

Keywords: contour generation, polar representation, data augmentation, computer vision, statistical characteristics, machine learning

Development of a genetic algorithm for solving a one-stage transport problem with fixed surcharges

2024. T.12. № 3. id 1624

Bondarenko Y.V. Goroshko I.V. Penzenskiy A.A.

DOI: 10.26102/2310-6018/2024.46.3.011

Managing complex logistics processes of modern enterprises requires the development of adequate mathematical models that make it possible to calculate optimal transportation plans. One of these models is the transport problem with fixed surcharges, the feature of which is the nonlinearity of the goal function. This study is devoted to the development of a genetic algorithm for solving a transport problem with fixed surcharges. The basis of the study is the analysis of existing approaches to solving various modifications of transport problems. A feature of the proposed algorithm is the formation at each stage of chromosomes that satisfy the constraints of the problem, which allows reducing the solution time. The study presents in detail the steps of the algorithm for forming the initial population, crossing over and mutation, adapted to the conditions of the transport problem with fixed surcharges. The formation of the initial population is based on the approach of randomly selecting a “supplier-consumer” pair, which ensures its sufficient diversity. The crossing over operator is implemented by developing an algorithm based on dividing modulo two the sum of the genes of the parents and subsequent redistribution of the remainders from the division between the descendants. The chromosome mutation algorithm is based on changing the transportation plan for randomly selected rows and columns while maintaining the admissibility of the individual. To conduct a computational experiment, a software product was developed in Python, and a demonstration example of the calculation is given. The results of the calculations for a group of agricultural producers allowed us to draw conclusions about the practical significance of the proposed algorithm and identified the possibilities of its use for solving multi-stage transport problems that are relevant for large manufacturing and logistics companies.

Keywords: transport problem, transport problem with fixed surcharges, genetic algorithm, chromosome, mutation, crossing over, heuristic algorithm, transportation plan, optimization

Estimation of the risk of developing chronic hepatitis C based on heuristic classification algorithms

2024. T.12. № 3. id 1623

Palevskaya S.A. Gushchin A.V. Ivanov D.V.

DOI: 10.26102/2310-6018/2024.46.3.020

The materials of the article are intended for specialists in the field of machine learning for the organization of technologies for improving the quality of information perception and interpretation of measurements in the practice of making medical decisions. The article proposes a method for selecting, tuning and testing a classifier under conditions of a deficit of a priori information in the data used. This is relevant when small samples of measurements of biological objects and their systems are formed at the initial stage of scientific research, the nonlinear properties of which often lead to the failure of statistical criteria. Nevertheless, the consistency of "inconvenient" distributions should be expressed in an adequate response of the program for assisting a medical decision. Based on this, the goal is determined - the choice of a solution method from the proposed set of methods for machine tuning of feature separation. Most tuning algorithms are heuristic, where the stop of parametric estimation is based on the criteria of entropy minimization as an indirect maximization of the received information. The main task is to determine the algorithm for learning and tuning the classification regression with an explicit predictive behavior of the similarity of the statistical convergence of the minimized errors. This guarantees an increase in the quality of risk classification with a trivial increase in training instances. The peculiarity of the case under consideration lies in the duality of the nature of chronic hepatitis C (CHC) progression in children: with HIV coinfection and CHC itself. This raises the problem of finding unified conditions for metric minimization of errors in еstimation the risk of developing CHC based on machine learning methods. Data sets were studied on small samples in order to identify significant parameters for heuristic identification of the presence of risks of developing the main and concomitant diseases. In this study, several methods of shallow machine learning of linear regressions were used, mainly using heuristic solutions for probabilistic separation of features. The article selectively describes the use of some basic learning methods taking into account their features in the technological verification of risk classifiers.

Keywords: machine learning, chronic hepatitis C, HIV coinfection, binary classifiers, lasso regression, sum of squared errors (MSE), regularization, decision Tree Classifier, ROC curve, area Under Curve (AUC)

Building dependencies of changes in the parameters of the child's body on age

2024. T.12. № 3. id 1622

Frolov S.V. Sudakov D.E. Starykh D.G.

DOI: 10.26102/2310-6018/2024.46.3.004

The relevance of the research conducted in the article is due to the need to predict the risks that threaten the life of a child with congenital heart disease in order to plan surgical interventions. The prognosis of the state of the cardiovascular system is based on a zero-dimensional model of blood circulation. To do this, it is proposed to create a quasi-stationary model in which the parameters of the cardiovascular system vary depending on the age of the child. Based on the analysis of data from regional monitoring of children's health, the article formulates a hypothesis that the parameters of a child's body change exponentially depending on age. Experimental studies based on monitoring data have confirmed the hypothesis put forward. A method for constructing changes in the parameters of a child's cardiovascular system depending on age is proposed and investigated. To establish this dependence, it is sufficient to have the parameter value for the child at the current time and at another time for the average child of a given gender. An algorithm for obtaining an experimental exponential dependence based on the use of Newton's iterative method for solving a nonlinear equation is proposed. The implementation of the proposed technique makes it possible to predict the state of the child's cardiovascular system for planning such interventions as surgical removal of congenital heart defects.

Keywords: congenital heart disease, approximation, exponential law, mathematical modeling, quasi-stationary system, cardiovascular system

Аn approach to the process of mutual information coordination of elements of data delivery systems based on an auction model

2024. T.12. № 3. id 1621

Rubtsov A.A.

DOI: 10.26102/2310-6018/2024.46.3.009

The article discusses the idea of increasing the efficiency of the process of servicing requests in peer-to-peer distributed computing systems based on the logical combination of their subset into peer-to-peer systems, and also proposes an algorithm for mutual information coordination of elements of the integrated system for servicing a flow of high-intensity requests for data based on the auction model. An auction model is proposed as a method and model that provides support for decentralized interaction between elements of a peer-to-peer system. The choice of the auction model – the inverse Vickrey auction model – is justified. Using the theory of multi-agent systems, an approach for the process of forming a logical group of elements of a peer-to-peer system is considered, and the corresponding software agent modules are identified that provide the functions of initializing and implementing the auction process. Using a set-theoretic representation, parameters are determined that form the conditions for the participation of nodes participating in the auction in the process of mutual information coordination, such as a cost function and a utility function. The choice and justification of the functions of the components of the auction model are considered in detail. The type of cost function and utility function used by the nodes participating in the auction is determined. Based on the composition of the functional components of the peering system elements included in the logical group, as well as determining the composition and type of functions implemented by these components, an algorithm for implementing the Vickrey reverse auction model has been developed, ensuring the formation and functioning of a logical group of peering system elements.

Keywords: distributed systems, data delivery system, peer-to-peer systems, queuing system, auction model

An algorithm for redistributing virtualized computing and communication resources of a data center based on ACS metaheuristics

2024. T.12. № 3. id 1620

Bumazhkina N.Y.

DOI: 10.26102/2310-6018/2024.46.3.005

The redistribution of virtualized computing and communication resources in data centers is a significant problem in the context of cloud technologies, making it difficult to ensure the stable functioning of services. These services must meet the criteria for quality of service, performance evaluation, and terms of service contracts imposed by cloud service providers. The main goal of the redistribution of virtualized computing and communication resources is the optimal placement of a subset of active virtual machines on a minimum number of physical machines, taking into account their multidimensional needs for computing and communication resources. Which will significantly improve the efficiency of a virtualized data center. The problem of redistributing computing and communication resources of a data processing center falls under the class of problems defined as "NP-hard" problems, since it involves a vast space of solutions. Therefore, more time is needed to find the optimal option. In previous studies of a number of such problems, it has been proven that metaheuristic strategies make it possible to find acceptable solutions in a suitable time. The article proposes to use a modified version of the ant colony metaheuristic algorithm to solve the problem of redistributing computing and communication resources between virtual machines of a data processing center, considered within the framework of the multidimensional vector packaging problem.

Keywords: virtualized computing and communication resources, metaheuristic methods, multidimensional vector packaging, ant colony optimization algorithm, data processing center

Application of the annealing method in the task of diagnosing electrical defects in analog circuits of radioelectronic devices

2024. T.12. № 4. id 1618

Uvaysov S.U. Chernoverskaya V.V. Nguyen Duc Hai Wo Thae Hai Pham Xuan Han

DOI: 10.26102/2310-6018/2024.47.4.022

Improving the methods of troubleshooting electronic devices remains an urgent and timely task at the current stage of development of this class of technical means. An electrical circuit that implements the functionality of an electronic device often contains elements whose parameters differ from the nominal values due to the peculiarities of the technological process of their production. This, in turn, may lead to a change in the output characteristics of the device, a malfunction or failure of an electronic device. The article presents the results of a study on the diagnosis of electrical defects in analog circuits of radioelectronic devices based on a modified algorithm for simulated annealing. The difficulties of applying the classical scheme of the algorithm and the impossibility of unambiguous identification of defects in electrical and radio elements are analyzed. A modified algorithm scheme is proposed that allows solving the optimization problem of finding the global extremum of the objective function for the problem of diagnosing the electronic component base. It is shown that for the algorithm to work effectively, it is necessary to correctly adjust its parameters and explore all possible options for generating neighboring solutions and temperature reduction mechanisms in order to choose the best implementation option. The annealing simulation algorithm has a number of advantages over other optimization algorithms. The operating time of the simulated annealing algorithm can be controlled using a cooling schedule. At the same time, an abrupt shutdown of the algorithm is allowed due to a change in the final temperature parameter. There is always a solution, no matter how much time has passed in the search process. This flexibility explains the widespread popularity of the annealing simulation algorithm in various fields of scientific research and applied problem solving.

Keywords: annealing simulation algorithm, optimal solution, radioelectronic device, defect diagnosis, electric radio element, global minimum, local minimum, mechanism for generating neighboring solutions, markov chain length, temperature reduction scheme